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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<base_pos: list<element: double>, base_quat: list<element: double>, parent: string, type: string>
to
{'base_pos': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'base_quat': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None)}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1492, in compute_config_parquet_and_info_response
fill_builder_info(builder, hf_endpoint=hf_endpoint, hf_token=hf_token, validate=validate)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 683, in fill_builder_info
) = retry_validate_get_features_num_examples_size_and_compression_ratio(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 602, in retry_validate_get_features_num_examples_size_and_compression_ratio
validate(pf)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 640, in validate
raise TooBigRowGroupsError(
worker.job_runners.config.parquet_and_info.TooBigRowGroupsError: Parquet file has too big row groups. First row group has 1894850886 which exceeds the limit of 300000000
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 797, in wrapped
for item in generator(*args, **kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 97, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 75, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in cast_array_to_feature
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2020, in <listcomp>
arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2025, in cast_array_to_feature
casted_array_values = _c(array.values, feature[0])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1804, in wrapper
return func(array, *args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<base_pos: list<element: double>, base_quat: list<element: double>, parent: string, type: string>
to
{'base_pos': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'base_quat': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None)}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1505, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1099, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
obj_file
dict | robot_file
dict | metadata
dict | plan
list | scene
dict | sequence
dict | trajectory
dict | scene_file
string | obstacles_file
string |
|---|---|---|---|---|---|---|---|---|
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":50369,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a0_","tasks":[{"algorithm":"","end":179.0,"name":"handover","object_index":0,"start":122.(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":2,"primitive":"pickpick1","robots":["a2_","a3_"]},{"object":2,"primitive":"pickp(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":16546,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a0_","tasks":[{"algorithm":"rrt","end":265.0,"name":"pick","object_index":1,"start":184.0(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":0,"primitive":"pickpick1","robots":["a3_","a2_"]},{"object":2,"primitive":"hando(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":9157,"folder":"out/run_id_202405271352/success/envId_614/rand(...TRUNCATED) | [{"robot":"a3_","tasks":[{"algorithm":"","end":218.0,"name":"handover","object_index":2,"start":165.(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":0,"primitive":"pick","robots":["a0_"]},{"object":2,"primitive":"handover","robot(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":31439,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a3_","tasks":[{"algorithm":"","end":73.0,"name":"handover","object_index":2,"start":28.0}(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":2,"primitive":"handover","robots":["a0_","a3_"]},{"object":1,"primitive":"pickpi(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":1442,"folder":"out/run_id_202405271352/success/envId_614/rand(...TRUNCATED) | [{"robot":"a0_","tasks":[{"algorithm":"rrt","end":147.0,"name":"pick","object_index":1,"start":73.0}(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":2,"primitive":"pick","robots":["a3_"]},{"object":0,"primitive":"pick","robots":[(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":52652,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a1_","tasks":[{"algorithm":"rrt","end":91.0,"name":"pick","object_index":2,"start":58.0},(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":1,"primitive":"handover","robots":["a3_","a0_"]},{"object":0,"primitive":"pick",(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":53760,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a0_","tasks":[{"algorithm":"","end":365.0,"name":"handover","object_index":1,"start":321.(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":2,"primitive":"handover","robots":["a2_","a3_"]},{"object":0,"primitive":"handov(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":32753,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a1_","tasks":[{"algorithm":"rrt","end":238.0,"name":"pick","object_index":2,"start":195.0(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":0,"primitive":"pick","robots":["a2_"]},{"object":1,"primitive":"handover","robot(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":2538,"folder":"out/run_id_202405271352/success/envId_614/rand(...TRUNCATED) | [{"robot":"a0_","tasks":[{"algorithm":"rrt","end":133.0,"name":"pick","object_index":0,"start":108.0(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":0,"primitive":"pickpick1","robots":["a3_","a0_"]},{"object":2,"primitive":"hando(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) | |
{"objects":[{"goal_pos":[-1.005004830126797,-0.4490026515700889,0.08],"goal_quat":[0.967977824185825(...TRUNCATED) | {"robots":[{"base_pos":[-0.5,-0.4,0.0],"base_quat":[1.0,0.0,0.0,0.0],"type":"ur5_vacuum"},{"base_pos(...TRUNCATED) | {"metadata":{"cumulative_compute_time":29896,"folder":"out/run_id_202405271352/success/envId_614/ran(...TRUNCATED) | [{"robot":"a2_","tasks":[{"algorithm":"","end":285.0,"name":"handover","object_index":0,"start":244.(...TRUNCATED) | {"Objects":{"obj1":{"goal":{"abs_pos":[-1.005004830126797,-0.3990026515700889,0.6299999999999999],"a(...TRUNCATED) | {"tasks":[{"object":2,"primitive":"handover","robots":["a0_","a3_"]},{"object":1,"primitive":"pickpi(...TRUNCATED) | {"objs":[{"name":"obj1","steps":[{"pos":[0.013139616294503442,0.2339829839707323,0.6299999999999999](...TRUNCATED) | "World \t{ X:<[0, 0, 0, 1, 0, 0, 0]> } \n\ntable_base (World) {\n Q:[0 0(...TRUNCATED) |
End of preview.